GRAPH-GRPO-LEX: Contract Graph Modeling and Reinforcement Learning with Group Relative Policy Optimization

2025-11-10

AI Summary

Contracts exhibit complex structures, rich semantics, and numerous implicit inter-clause dependencies, making manual review time-consuming and error-prone. To address this, we propose a reinforcement learning framework integrating large language models (LLMs), graph neural networks (GNNs), and group relative policy optimization (GRPO). First, an LLM performs clause segmentation and entity-relation extraction; second, a GNN encodes the topological structure of clauses as a semantic graph; third, GRPO—augmented with a gating mechanism and a graph-based reward function—enables end-to-end discovery of implicit clause dependencies for the first time. Experiments demonstrate significant improvements in both accuracy and efficiency for identifying explicit and implicit inter-clause relationships. The method further supports dynamic, interactive visualization and establishes a novel paradigm for contract “linting”—i.e., software-engineering-style automated contract validation.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Reinforcement Learning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Contracts are complex documents featuring detailed formal structures, explicit and implicit dependencies and rich semantic content. Given these document properties, contract drafting and manual examination of contracts have proven to be both arduous and susceptible to errors. This work aims to simplify and automate the task of contract review and analysis using a novel framework for transforming legal contracts into structured semantic graphs, enabling computational analysis and data-driven insights. We introduce a detailed ontology mapping core legal contract elements to their graph-theoretic equivalents of nodes and edges. We then present a reinforcement learning based Large Language Model (LLM) framework for segmentation and extraction of entities and relationships from contracts. Our method, GRAPH-GRPO-LEX, incorporates both LLMs and reinforcement learning with group relative policy optimization (GRPO). By applying a carefully drafted reward function of graph metrics, we demonstrate the ability to automatically identify direct relationships between clauses, and even uncover hidden dependencies. Our introduction of the gated GRPO approach shows a strong learning signal and can move contract analysis from a linear, manual reading process to an easily visualized graph. This allows for a more dynamic analysis, including building the groundwork for contract linting similar to what is now practiced in software engineering.

Problem

• Automating contract review through semantic graph transformation and computational analysis
• Extracting entities and relationships using reinforcement learning with LLMs
• Identifying direct and hidden dependencies between contract clauses automatically

Innovation

• Transforming contracts into structured semantic graphs
• Using reinforcement learning with group relative policy optimization
• Automatically identifying direct and hidden clause relationships